1863 papers
This paper proposes a vessel-aware deep learning framework for detecting age-related macular degeneration (AMD) in OCTA images by integrating external multiplicative attention with clinically meaningful vascular biomarkers, specifically tortuosity and dropout maps, to guide the model toward physiologically relevant regions and improve interpretability.
This paper introduces an efficient framework for heterogeneous decentralized diffusion models that enables experts to train with mixed objectives (DDPM and Flow Matching) and reduced resource requirements, achieving a 16x decrease in compute and 14x reduction in data compared to prior approaches while improving image quality and diversity.
ButterflyViT introduces a geometric parameterization method that treats Mixture of Experts as rotations of a shared quantized substrate, achieving a 354 memory reduction for Vision Transformers on edge devices while maintaining accuracy through spatial smoothness regularization.
This paper introduces XMACNet, an explainable, lightweight CNN that combines self-attention mechanisms with multi-modal fusion of RGB images and vegetation indices to achieve high-accuracy chili disease classification suitable for edge deployment.
This paper introduces EarthBridge, a high-fidelity cross-modal translation framework combining Diffusion Bridge Implicit Models and Contrastive Unpaired Translation to achieve second place in the 4th Multi-modal Aerial View Image Challenge by effectively translating between SAR, EO, and IR aerial imagery.
The paper proposes HiDE, a hierarchical dictionary-based entropy modeling framework for learned image compression that enhances coding efficiency by decomposing external priors into global and local dictionaries with cascaded retrieval and employing a context-aware parameter estimator to achieve significant BD-rate savings over state-of-the-art methods.
This paper presents a hybrid machine learning model that combines VGG19, EfficientNet, and ResNet50 for feature extraction with a Bi-LSTM classifier to achieve a 98.83% accuracy in the early detection of Cerebral Palsy from MRI images, outperforming several individual pre-trained models.
This paper establishes that the quality of a model's step-level visual grounding, quantified by the Step Grounding Rate (SGR), serves as a robust and independent predictor of out-of-distribution generalization in long-horizon vision-language models, outperforming traditional final-answer accuracy metrics.
This paper introduces MotionBits, a novel concept and learning-free segmentation method that identifies the smallest manipulable rigid bodies through kinematic spatial twist equivalence, outperforming state-of-the-art embodied perception models on the new MoRiBo benchmark and enabling more effective downstream robotic manipulation and reasoning tasks.
This paper introduces Perturbed Gaussian Ensemble, an active view selection framework for sparse-view CT that leverages stochastic density scaling of uncertain Gaussian primitives to identify high-variance projections, thereby significantly improving reconstruction fidelity and reducing geometric artifacts compared to existing methods.
This paper introduces an extended 3-manifold topological model for high-contrast optical flow that resolves the limitations of previous torus-based approaches by identifying that the most significant motion patches are concentrated near binary step-edge circles rather than the torus, thereby offering new insights into the topological and geometric structures underlying visual data inference.
This paper introduces ColonSplat, a dynamic Gaussian Splatting framework that achieves superior 3D reconstruction of peristaltic colon motion by preserving global geometric consistency, supported by a new synthetic benchmark dataset called DynamicColon and a critical analysis of existing methods' limitations.
This paper introduces IGLU, a novel parametric activation function derived from a scale mixture of GELU gates that utilizes a Cauchy CDF to provide heavy-tailed gradient properties and robustness against vanishing gradients, alongside a computationally efficient rational approximation (IGLU-Approx) that achieves competitive or superior performance across vision and language tasks compared to standard baselines like ReLU and GELU.
This paper proposes a hardware-efficient trajectory prediction framework that integrates environmental priors with a Dual-Transformer-Cascaded (DTC) architecture to accurately predict the landing points of flying objects, such as tennis balls, by outperforming existing methods in complex real-world scenarios.
The paper introduces PICS, a self-supervised framework that improves pairwise image compositing by employing an Interaction Transformer with mask-guided Mixture-of-Experts and adaptive blending to explicitly model spatial interactions and preserve physical consistency between objects and backgrounds.
This paper introduces OPTED, an open-source preprocessed trachoma eye dataset derived from 2,832 images using a zero-shot SAM 3 pipeline to automatically extract and standardize regions of interest, thereby addressing the scarcity of high-quality data for automated trachoma classification in Sub-Saharan Africa.
This paper proposes a novel data augmentation paradigm that leverages Large Language Models to generate diverse, industry-resembling CAD programs by conditioning them on reference surfaces and modeling procedures, thereby addressing the scarcity of complex, spline-based geometric data in existing training sets.
PaQ-DETR is a unified object detection framework that addresses query utilization imbalance by dynamically generating image-specific queries from shared latent patterns and employing a quality-aware one-to-many assignment strategy, resulting in consistent mAP improvements across various DETR backbones.
The paper proposes DLRMamba, a novel framework for edge-based multispectral object detection that combines a Low-Rank SS2D module to reduce parameter redundancy with a Structure-Aware Distillation strategy to preserve feature fidelity, achieving superior efficiency and accuracy on resource-constrained hardware.
This paper introduces ESM-YOLO+, a lightweight visible-infrared fusion network that employs a Mask-Enhanced Attention Fusion module and training-time Structural Representation enhancement to achieve high-precision small-target detection in complex remote sensing scenes while significantly reducing model complexity compared to baselines.